Measurement evaluation device

ABSTRACT

A device ( 1 ) for evaluating a quality of measurements, the device ( 1 ) comprising a data store storing a set of n network nodes N i , wherein said nodes store measurement data. The data store further comprises a set of l network links L ij , wherein each of the network links L ij  connects one network node Ni with at least one network node N j . The device ( 1 ) further comprises a quality evaluation unit ( 12 ) for calculating said quality, which quality evaluation unit ( 12 ) is adapted and structured to calculate a quality score Q as a function of said links L ij .

TECHNICAL FIELD

The present invention relates to a device for evaluating a quality of measurements and a method for evaluating measurements with the device.

BACKGROUND ART

To verify a distinct measurement result, large amounts of measurement data might be collected which data needs to be analyzed and archived.

Regarding the state of the art, current device architectures for evaluating measurement data are simple storage devices, which store the data in a set of measurement data in a database. If large amounts of measurement data are collected, it can become very difficult to analyze the sets of measurement data and to verify, which measurement data is relevant in view of the whole set of measurement data, e.g. which data is confirming or contradicting a distinct measurement result of the set. The verification can become even more complex if multiple sources add measurement data to such a storage device.

DISCLOSURE OF THE INVENTION

The problem to be solved by the present invention is therefore to provide a device that enables the evaluation of sets of measurement data.

This problem is solved by a device for evaluating a quality of measurements, the device comprising a data store storing a set of n network nodes wherein said nodes store measurement data. Optionally, each node is attributed to at least one of a number s of subsets S_(k). In addition to the data store, the device preferably comprises a server to connect the device with a public network and to exchange data therewith, such that users of the network can access the device and generate said nodes N_(i), N_(j).

The subsets S_(k) are preferably each assigned to a source U_(k), which source U_(k) is preferably a user of the device, a group of users of the device, or a specific measurement equipment that contributes to the measurement data that is stored in the nodes N_(i), N_(j) of the subset S_(k).

If subsets are used, each subset S_(k) can contain only one single node N_(i) or can contain more than one node N_(i), N_(j).

The data store is further comprising a set of l network links L_(ij), wherein each of the network links L_(ij) connects one network node N_(i) with at least one network node N_(j), wherein each link L_(ij) comprises a linking value v_(ij) indicative of a correspondence between said nodes N_(i) and N_(j). Preferably, the linking value v_(ij) is larger for a higher correspondence between the nodes N_(i) and N_(j) as compared to the linking value v_(ij) for a lower correspondence between said nodes N_(i) and N_(j). The correspondence between the two nodes N_(i) and N_(j) can be dynamic over time, such that the linking value v_(ij) can change over time. The linking values v_(ij) are preferably scalar values, but can also be vector values in a node coordinate system. The nodes N_(i), N_(j) and links L_(ij) are preferably programmed within a software setting of the data store.

In addition, the device comprises a quality evaluation unit for calculating the quality, which quality evaluation unit is adapted and structured to calculate a quality score Q of measurements as a function of the links L_(ij). The quality evaluation unit is preferably linked to the data store. The quality score Q can be calculated as Q_(Sk) for a subset S_(k) of the s subsets S_(k) and/or as Q_(Ni) for a node N_(i) of the set of nodes N_(i), N_(j), in both cases as a function of the links L_(ij). The quality factor Q is preferably a scalar value.

In a further aspect of the invention, the device comprises, in addition, a resource allocation unit for allocating computing units, bandwidth or storage resources for processing the nodes as a function of the quality score Q. The resource allocation unit is preferably connected to the data store and the storage and computing units of the device and receives information regarding the quality scores Q of the individual nodes N_(i) or subsets S_(k) from the quality evaluation unit.

The resource allocation unit preferably selects the number of resources (such as a number of computing units, a processing or networking bandwidth or a storage performance) to be used for processing a given node N_(i), N_(j) or a subset of the nodes as a function of the quality score Q, in particular of the quality score Q_(Ni) of the measurement data of a node N_(i) and/or of the quality score Q_(Sk) of the measurement data of a subset S_(k).

Preferably, the device comprises a plurality of computing units, wherein the resource allocation unit is adapted and structured to allocate a number z of said computing units for processing a given node. The associated number z depends on the quality score Q, in particular on the quality score Q_(Ni) of the measurement data of a node N_(i) and/or on the quality score Q_(Sk) of the measurement data of a subset S_(k).

The computing units form a logical part of the device. They may be centralized or decentralized.

Allocating the number z of computing units preferably relates to assigning a certain number of central processing units (CPUs) for processing the particular node. Processing the nodes e.g. relates to the evaluation of the quality of the measurement data, in particular by calculating the quality score Q. In addition, the processing of the node can comprise the assignment of the measurement data to a node N_(i) in a set of nodes N_(i), N_(j) and/or the attributing of subsets S_(k) to the nodes N_(i), N_(j). Data processing can further comprise the connecting of the nodes N_(i), N_(j) by links L_(ij), and further indicating the correspondence between the nodes N_(i) and N_(j) to assign a linking value v_(ij) to each link L_(ij). Data processing can also comprise the retrieval and/or the displaying of a node on a display device.

In addition, the resource allocation unit of a preferred device is adapted and structured to allocate bandwidth resources to the node, depending on the quality score Q. In particular, the allocation of the bandwidth resources for a node N_(i) is dependent on the quality score Q_(Ni) of the node N_(i) and/or on the quality score Q_(Sk) of the subset S_(k) that the node N_(i) is attributed to. Bandwidth resources preferably relate to the amount of data that is exchanged in a given time, e.g. for retrieving nodes or transmitting measurement data in nodes, e.g. between the computing units and storage resources or between the device and remote objects.

A preferred embodiment of the device comprises a plurality of storage resources, in particular storage resources with different performance. The resource allocation unit is adapted and structured to select the storage resources for storing a given node depending on the quality score Q. In particular, the selection of the storage resources is dependent on the quality score Q_(Ni) of the node N_(i) and/or on the quality score Q_(Sk) of a subset S_(k) that the node N_(i) is attributed to.

Storage resources preferably refer to hardware storage that forms a logical part of the device. Storage resources can be centralized or decentralized.

As mentioned before, the quality evaluation unit for calculating the quality of measurements is adapted and structured to calculate the quality score Q as a function of the links L_(ij).

As already indicated, the nodes can belong to certain subsets S_(k) of nodes, which can e.g. describe the source, e.g. an authorship of the data, of a node or a measurement equipment used to generate the data in the node. The number of said subsets is in the following designated as s. In that case, the data store is advantageously structured and designed to store, for each of said nodes, information indicative of at least one subset S_(k) of nodes that a node belongs to. Further, the evaluation unit is adapted and structured for calculating a subset quality score Q_(Sk) at least for said subset S_(k) as a function of said links L_(ij). This subset quality score Q_(Sk) forms a specific embodiment of said quality score Q and describes the quality of the subset S_(k).

As mentioned, a subset S_(k) can comprise no node, a single node or several nodes.

Alternatively or in addition thereto, the quality evaluation unit (12) is adapted and structured for calculating a node quality score Q_(Ni) for at least one node N_(i) of said nodes as a function of said links L_(ij). This node quality score Q_(Ni) forms another specific embodiment of said quality score Q, and it describes the quality of the node N_(i).

In other words, the quality score Q can be calculated for a single node N_(i) of a set of nodes N_(i), N_(j) or for a subset S_(k) of the nodes N_(i), N_(j).

The links L_(ij) between the nodes N_(i), N_(j) can comprise directional links indicating that a succeeding node N_(j) depends on a seeding node N_(i). For example, such a directed link can represent the relationship between two measurements, wherein measurement data stored in the seeding node N_(i) led to a follow-up measurement, which data is stored in succeeding node N_(j). In a further example, the data stored in a succeeding node N_(j) might be based on results or methods used in a measurement which data is attributed to the seeding node N_(i), and thus causing a directed relationship. The quality score Q_(i) of at least one node N_(i) of the set of nodes N_(i), N_(j) in such a network with directional Links L_(ij) is impacted by a seeding potential of the particular node N_(i), which is quantitatively defined by a node seeding potential NSP_(i) of the node N_(i) given by a formula (I) as

NSP_(i) =|C _(i)|  (I),

wherein C_(i) refers to a set of nodes containing all nodes N_(j) depending directly or indirectly on the node N_(i) to which they are linked via the directional links L_(ij). The operator | . . . | designates the cardinality of the set C_(i), i.e. the number of elements N_(j) in the set. The quality score Q_(Ni) of the nodes N_(i) is preferably impacted by the NSP_(i) of the node N_(i).

The node seeding potential NSP_(i) is an example for a node quality score Q_(Ni).

To evaluate the seeding potential not only for a single node N_(i), but also for a subset of nodes N_(i), N_(j), the quality evaluation unit is preferably structured to further calculate a source seeding potential SSP_(k) of the subset S_(k) as a function of the node seeding potential NSP_(i) of the nodes N_(i) contained in the subset S_(k), given by a formula (II) as

$\begin{matrix} {{SSP}_{k} = {\sum\limits_{N_{i}\; {in}\mspace{11mu} S_{k}}{{NSP}_{i}.}}} & ({II}) \end{matrix}$

The source seeding potential SSP_(k) is an example for a source quality score Q_(Sk).

More generally, the source quality score Q_(Sk) of the subset S_(k) is preferably a function of the source seeding potential SSP_(k). Thereby, the quality of measurements from a specific source U_(k), which contributed the measurement data in the nodes N_(i), N_(j) of the subset S_(k), can be evaluated, e.g. a first source U₁ referring to a first subset S₁ can be evaluated in comparison with a second source U₂, referring to a second subset S₂, by comparing the quality scores Q_(S1) and Q_(S2) of each subset S₁, S₂.

A further embodiment of the present invention assesses the impact of the linking values v_(ij) on the quality score Q for a particular node N_(i) of a set of nodes N_(i), N_(j). Therefore, the quality evaluation unit is adapted and structured to evaluate a node bridging potential NBP_(i) of at least one node N_(i) of the set of nodes N_(i), N_(j). The node bridging potential NBP_(i) is given by formula (III) as

NBP_(i) =P(M _(i))−P(M′ ₁)  (III)

with M₁ being a set of links L_(m) containing all links L_(ij) of the set of links L_(ij) that connect the nodes N_(j) of the set of nodes N_(i), N_(j) directly or indirectly to said node N_(i), and with being said set M_(i) of links L_(m) without the links L_(ij) to said node N_(i). P is a function of a set X of links given by formula (IV) as

$\begin{matrix} {{P(X)} = {\sum\limits_{{links}\mspace{14mu} L_{ij}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {set}\mspace{14mu} X}{v_{ij}.}}} & ({IV}) \end{matrix}$

The NBP_(i) assesses not only the impact of the linking values v_(ij), but also provides an indication of the bridging potential of an individual node N_(i), viz. it correlates to a number of branches the node N_(i) is linking with each other and increases with an increasing number thereof.

The node seeding potential NBP_(i) is a further example for a node quality score Q_(Ni).

To account for the bridging potential of individual sources U_(k) or, more generally, of subsets S_(k), the quality evaluation unit is preferably adapted and structured to calculate the quality score Q_(Sk) as a function of the source bridging potential SBP_(k) of a subset S_(k) of the s subsets S_(k). The source bridging potential SBP_(k) is thereby given by formula (V) as

$\begin{matrix} {{{SBP}_{k} = {\sum\limits_{{k^{\prime} = 1},\ldots,{s\mspace{14mu} {without}\mspace{14mu} k}}\left( {{P\left( T_{k} \right)} - {P\left( T_{k^{\prime}}^{\prime} \right)}} \right)}},} & (V) \end{matrix}$

with T_(k) being a set containing all links L_(ij) of the set of links L_(ij) that connect nodes N_(i) of the set of nodes N_(i), N_(j) directly or indirectly to at least one node N_(i) in said subset S_(k). T′_(k′) contains the links of said set T_(k), but without the links L_(ij) directly connected to at least one node N_(i) in said subset S_(k), and wherein the links L_(ij) of T′_(k′) connect directly or indirectly to nodes N_(i) of the subset S′_(k′). P is the function of a set X of links L_(ij) given by formula (VI) as

$\begin{matrix} {{P(X)} = {\sum\limits_{{links}\mspace{14mu} L_{ij}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {set}\mspace{14mu} X}{v_{ij}.}}} & ({VI}) \end{matrix}$

The source bridging potential SBP_(k) accounts for the impact the individual subsets S_(k) have on the quality score Q and reflects the bridging potential of the individual subsets S_(k) and therefore the individual sources U_(k) within the set of linked nodes N_(i), N_(j).

The source bridging potential SBP_(k) is a further example for a source quality score Q_(Sk).

The quality evaluation unit is preferably not only adapted and structured to calculate the quality score Q, but also to evaluate a maximal node quality score Q_(Ni,max) from a set of quality scores comprising a first node quality score Q_(N1) of a first node N₁ and a second node quality score Q_(N2) of a second node N₂. In addition, the quality evaluation unit can further evaluate the maximal subset quality score Q_(Sk,max) from a subset S_(k) of s subsets S_(k) comprising a first subset quality score Q_(S1) of a first subset S₁ and a second subset quality score Q_(S2) of a second subset S₂. Furthermore the quality evaluation unit can also calculate a quality score of a particular node N_(i) in a particular subset S_(k), e.g. by adding or multiplying the node quality score Q_(Ni) of the node N_(i) of the set S_(k) with the subset quality score Q_(Sk) of the set S_(k).

Preferably, the quality evaluation unit is further structured to calculate the quality score Q as a combination of one or more of the node seeding potential NSP, the source seeding potential SSP, the node bridging potential NBP, and/or the source bridging potential SBP.

More generally, the quality evaluation unit is further structured to calculate at least a first quality score Q of a first node or subset and a second quality score Q of a second node or subset, and to evaluate a node or subset S_(max) with a highest quality score Q_(max).

In a preferred embodiment of the invention, the device for evaluating a quality of measurements is further configured to return a quality score Q of a node N_(i) comprising measurement data and/or of the subset S_(k) comprising the node N_(i) to the source of the measurement data, e.g. to users of a public network or to measurement equipment which collected the data.

A further aspect of the invention relates to a method for evaluating the measurements using the device for evaluating a quality of measurements. The method comprises the steps of storing the measurement data in the nodes N_(i), N_(j) and storing the links L_(ij) that connect one network node N_(i) with a network node N_(j). The quality score Q is further calculated for evaluating the quality of the measurement data. Preferably, the source quality score Q_(Sk) of the subset S_(k) of the nodes N_(i), N_(j) is calculated as a function of the links L_(ij), for evaluating the quality of the measurement data in the subset S_(k). Also, the node quality score Q_(Ni) of the node N_(i) of the set of nodes N_(i), N_(j) is preferably calculated as a function of the links L_(ij), for evaluating the quality of the measurement data in the particular node N_(i). A preferred method comprises the further steps of executing a number of measurements, by means of several sources U_(k), and storing measurement data from the sources U_(k) in the nodes N_(i), N_(j), wherein the subsets S_(k) correspond to the sources U_(k), and wherein the nodes N_(i), N_(j) are linked by the network links L_(ij).

A preferred step of the method is to send the quality score Q_(Ni) of the nodes of the subset and the quality score Q_(Sk) of the subset itself to the source of the measurement data attributed to the nodes of the subset after the evaluation of the measurement data.

In a preferred embodiment of the invention, the source or, in particular, the user corresponds to an author of a scientific observation, and the scientific observation corresponds to the measurement data stored in a node of the set. An author making several such scientific observations can store each scientific observation and related information in a new node of the set and link it to at least one of the author's earlier observations by confirmatory or contradictory indications of the links, thus creating a storyline of his observations.

Other advantageous embodiments are listed in the dependent claims as well as in the description below.

As used herein, the term “contain” is used in its limiting sense, i.e. a “set containing elements” is a set containing exactly and only said elements.

It is understood that the various embodiments and preferences as provided in this specification may be combined at will.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention, aspect and advantages will become apparent from the following detailed description thereof. Such description makes reference to the annexed drawings, wherein the figures show:

FIG. 1 a schematic of an architecture of the device for evaluating a quality of measurements with its peripherals according to an embodiment of the invention,

FIG. 2a a semantic network of a set of nets work nodes N_(i), N_(j) linked by network links L_(ij), wherein the links L_(ij) are directional links indicating that node N_(j) is depending on node N_(i) according to an embodiment of the invention,

FIG. 2b a semantic network of a set of network nodes N_(i), N_(j), wherein links L_(ij) connecting the node N_(i) with the node N_(j) comprise linking values v_(ij) indicating a correspondence between the nodes N_(i) and N_(j) according to an embodiment of the invention, and

FIG. 2c a semantic network of the correspondence between different subsets S_(k) according to an embodiment of the invention.

MODES FOR CARRYING OUT THE INVENTION

FIG. 1 shows a device architecture of a preferred embodiment of the invention. The device 1 can be formed by one or more physical or virtual servers and databases and a data network linking the same.

In the example shown, there is a primary server 2 containing a primary database 11 and a primary set of computing units 13. In addition, device 1 of the embodiment of FIG. 1 comprises secondary database 32 and secondary computing units 31, e.g. installed at locations remote from primary server 2.

Device 1 maintains a network 10 of nodes, wherein each measurement data (or other type of data) added to the network 10 generates a new entry in the network 10 as a new network node N_(i) in a set of n nodes N_(i).

Network 10 is stored in the databases 11, 32, which together form the data store of the device.

The data store stores the measurement data, nodes N_(ij), links L_(ij) and linking values v_(ij). The computing units 13, 31 process the same.

The device 1 is typically connected to a public network 3, such that users U_(k) of this network, e.g. the three users U₁, U₂, and U₃ of FIG. 1, can store their measurement data as well as other types of observations as nodes in the data store of device 1. All nodes N_(i) originating from one user U_(k) out of the set of n nodes N_(i), N_(j) are attributed to a subset S_(k), e.g. to S₁, S₂, or S₃ for users U₁, U₂, and U₃. In other words, the nodes are attributed to several subsets, and these subsets can e.g. represent the contributing users of the system.

The subset(s) that each node belongs to are also stored in the data store of device 1.

The data store further stores a set of network links L_(ij), wherein each of the network links L_(ij) connects one network node N_(j) with at least one network node N_(j). Each link L_(ij) comprises a linking value v₁₃ being indicative of a correspondence between the nodes N_(i) and N_(j).

Typically, there exist links between only some of the nodes in the network, i.e. one node is typically connected to only part of the other nodes. Some nodes may not have any links at all.

In addition to the data store, the device 1 comprises a quality evaluation unit 12, for calculating a quality of the measurements. The quality evaluation unit 12 is adapted and structured to calculate a “quality score Q” as a function of the links L_(ij). The quality evaluation unit is preferably implemented as a software for the network 10. It can e.g. be running on the processing units 13 for accessing the network and for processing the calculation of the quality score.

In addition, the device 1 comprises a resource allocation unit (RAU) 14, which can e.g. also be a software running on the processing units 13.

The resource allocation unit 14 can allocate bandwidth resources within the device 1 by means of a bandwidth control unit (BCU) 15. Allocating bandwidth resources to a node N_(i) or to a subset S_(k) can include the reservation of a certain bandwidth for data transfer to or from the node N_(i) or subset S_(k), e.g. for editing, displaying or downloading the data stored in the particular node N_(i) or in a node N_(i) of the subset S_(k). The resource allocation unit 14 allocates the bandwidth resources for a particular node N_(i) or a subset S_(k) depending on its quality score Q.

The resource allocation unit 14 can further allocate a number z of said computing units 13, 31 for processing a given node or subset of nodes by means of a computing control unit (CCU) 16. In general, the resource allocation unit 14 allocates the computing resources for a particular node N_(i) or a subset S_(k) depending on its quality score Q.

The resource allocation unit 14 can further select the database(s) or (in more general terms) the storage resources to be used for storing a given node by means of a database control unit (DCU) 17. The resource allocation unit 14 allocates the storage resources for a particular node N_(i) or a subset S_(k) depending on its quality score Q.

As mentioned, the computing units 13, 31, databases 11, 32 and bandwidth resources are allocated for a particular node N_(i) or a subset S_(k) depending on their quality score Q. Nodes N_(i) with a high quality score Q_(Ni) and subsets S_(k) with a high quality score Q_(Sk) can benefit from more and better resources, e.g. faster exchange of data to the particular storage resource where the data measurements attributed to the particular node N_(i) or subset S_(k) are stored, or allocation of more computing units 13, 31 or bandwidth for faster processing of the data.

For example, a higher number z of computing units 13, 31, a larger amount of bandwidth and/or a faster storage resources 11, 32 can be provided by the resource allocation unit 14 for a node if the quality score Q exceeds a certain threshold or if a first quality score Q₁ exceeds a second quality score Q₂.

FIG. 2 shows an example of an evaluation of the quality of the nodes, in particular of the measurement data in the nodes, for a simple case comprising three subsets S₁, S₂, and S₃. FIGS. 2a and 2b show each the same topology of the network 10 with a set of network nodes N_(i), N₂, N₃, N₄, and N₅, wherein each node is attributed to one subset S₁, S₂, or S₃ such that S₁{N₁, N₃}, S₂={N₂}, and S₃={N₄, N₅}. The affiliation is illustrated in the figure, such that nodes of the subset S₁ are illustrated as circles, nodes of subset S₂ are illustrated as squares, and nodes of subset S₃ are illustrated as triangles.

In the preferred embodiment of the invention shown in FIG. 2a , the links L_(ij) are directional links indicating that a node N_(i) depends on a node N_(j), which linking direction is indicated by arrows in FIG. 2a . The link L₁₂ linking node N₁ and node N₂ is directed from node N₁ to node N₂, link L₂₃ is directed from node N₂ to node N₃, link L₂₄ is directed from node N₂ to node N₄, and link L₄₅ is directed from node N₄ to node N₅. The direction of the links L_(ij) refers to the relationship between the linked nodes N_(i) and N_(j). A directed link can have different effects on the two nodes that it connects.

For example, if the measurement data stored in one node N_(i) inspired a user of the device to collect the measurement data in node N_(j), node N_(j) is considered to be a successor node of a seeding node N_(i) and thus the link between the two is directed from seeding node N_(i) to successor node N_(j). Such a relationship can also relate to the use of a same method or a same probing sample for an experiment collecting measurement data comprised in nodes N_(i) and N_(j).

For each node N₁, N₂, N₃, N₄, and N₅ the quality evaluation unit 12 can calculate a node quality score Q_(N1), Q_(N2), Q_(N3), Q_(N4), and Q_(N5) respectively and/or for each subset S₁, S₂, and S₃, a respective subset quality score Q_(S1), Q_(S2), and Q_(S3) as a function of the links L_(ij).

An example of a node quality score is the node seeding potential. A node seeding potential NSP_(i) is an indicator on how many successor nodes N_(j) are directly or indirectly succeeding from a seeding node N_(i). The quality evaluation unit is adapted and structured to calculate this node seeding potential NSP_(i) for a node N_(i) by calculating the cardinality of the set of succeeding nodes N_(j) of a node N_(i) with the formula (I)

NSP₁ =|C _(i)|,  (I)

wherein C_(i) is a set of nodes containing all nodes N_(j) depending directly or indirectly on the seeding node N_(i). Therefore, it follows for the nodes seeding potentials NSP_(i) of this example:

C ₁ ={N ₂ ,N ₃ ,N ₄ ,N ₅}⇒NSP₁ =|C ₁|=4

C ₂ ={N ₃ ,N ₄ ,N ₅}⇒NSP₂ =|C ₂|=3

C ₃={ }⇒NSP₃ =|C ₃|=0

C ₁ ={N ₅}⇒NSP₄ =|C ₄|=1

C ₅={ }⇒NSP₅ =|C ₅|=0

According to the calculations, node N₁ has the highest node seeding potential NSP. A higher seeding potential NSP_(i) of a node N_(i) indicates that a higher number of directly and indirectly succeeding nodes N_(j) that are related to the node N_(i).

To assess the impact of a subset S_(k) of nodes N_(i) on the quality score Q_(i), the quality evaluation unit 3 is structured to calculate a source seeding potential SSP of each set S_(k). The source seeding potential SSP is an example of a subset quality score, and it can be calculated as a function of formula (II)

$\begin{matrix} {{SSP}_{k} = {\sum\limits_{N_{i}\mspace{14mu} i\; n\mspace{14mu} S_{k}}{{NSP}_{i}.}}} & ({II}) \end{matrix}$

For each of the example subsets S₁, S₂, and S₃, the SSP_(k) can therefore be calculated as following:

${SSP}_{1} = {{\sum\limits_{N_{i}\mspace{14mu} i\; n\mspace{14mu} S_{1}}{NSP}_{i}} = {{{NSP}_{1} + {NSP}_{3}} = 4}}$ ${SSP}_{2} = {{\sum\limits_{N_{i}\mspace{14mu} i\; n\mspace{14mu} S_{2}}{NSP}_{i}} = {{NSP}_{2} = 3}}$ ${SSP}_{3} = {{\sum\limits_{N_{i}\mspace{14mu} i\; n\mspace{14mu} S_{3}}{NSP}_{i}} = {{{NSP}_{4} + {NSP}_{5}} = 1}}$

Subset S₁ has the highest source seeding potential. A higher source seeding potential SSP_(k) of a subset S_(k) indicates that a higher number of directly and indirectly succeeding nodes N_(j) are related to a node N_(i) of a subset, but in addition also assesses if a subset comprises multiple seeding nodes N_(i) which relate to succeeding nodes N_(j). The source seeding potential SSP_(k) of a subset impacts the calculation of the source quality score Q_(Sk) of the respective subset, i.e. the quality score is advantageously a function of the source seeding potential of a subset.

In a further embodiment of the invention shown in FIG. 2b , each link L_(ij) comprises a linking value v_(ij) that is indicative of a correspondence between the nodes N_(i) and N_(j). In contrast to the embodiment of FIG. 2a , the links L_(ij) are non-directional, i.e. each link has typically the same effects on both nodes that it is connected to.

Node N₁ is linked via link L₁₂ to node N₂, wherein the link L₁₂ comprises a value v₁₂. The node N₂ is linked to N₃ via link L₂₃ with value v₂₃ and to node N₄ via link L₂₄ with value v₂₄. The node N₄ is again linked to node N₅ via link L₄₅ with value v₄₅. Preferably, such a correspondence refers to a confirmatory or contradictory indication between the node N_(i) and N_(j). For example, if the measurement data stored in N_(i) is confirmed by a further measurement data stored in N_(j), the correspondence between the nodes N_(i) and N_(j) could be positive and therefore a linking value v_(ij)=(+1) could be assigned. In a further example, if the measurement data stored in N_(i) is contradictory to the measurement data stored in N_(j), the correspondence between the nodes N_(i) and N_(j) would be negative and a linking value v_(ij)=(−1) would be assigned. In further examples, the linking values v_(ij) could be further assessed by a level of agreement or disagreement of the measurement data corresponding to the nodes N_(i) and N_(j), which level would be expressed by linking values v_(ij)>(+1) for a high level of agreement and linking values v_(ij)<(−1) for a high level of disagreement. In addition, the linking value v_(ij) of a link L_(ij) between the node N_(i) and the node N_(j) can also be neutral, taking on a value of v_(ij)=0, to indicate that there is a relationship between node N_(i) and node N_(j), but that neither a confirmation nor a contraction has been found. A higher linking value v_(ij) is defined by having a higher numerical value than a lower linking value v_(ij). Hence, in this case the linking values are advantageously scalars.

To evaluate the correspondence of the particular node N_(i) with the nodes of the set of nodes N_(i), N_(j), the quality evaluation unit is adapted and structured to calculate a node bridging potential NBP_(i) of the node N_(i), wherein the node bridging potential NBP_(i) is given by formula (III) as

NBP_(i) =P(M _(i))−P(M′ _(i))  (III)

with M_(i) being a set of links L_(m) containing all links L_(ij) that connect the nodes N_(j) directly or indirectly to said node N_(i). M′_(i) is said set M_(i) of links L_(m) without the links L_(ij) linked directly to said node N_(i). P is a function of a set X of links given by formula (IV)

$\begin{matrix} {{P(X)} = {\sum\limits_{{links}\mspace{14mu} L_{ij}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {set}\mspace{14mu} X}{v_{ij}.}}} & ({IV}) \end{matrix}$

For the present example, the linking values v_(ij) are assumed to have the values [v₁₂, v₂₃, v₂₄, v₄₅] =[1, 1, 1, 1]. The node bridging potentials NBP_(i) for the nodes N₁, N₂, N₃, N₄, and N₅ are then calculated as following:

NBP₁ =P(M ₁)−P(M′ ₁)=4−3=1

with M₁={L₁₂, L₂₃, L₂₄, L₄₅} and P(M₁)=4

-   -   M′₁={L₂₃, L₂₄, L₄₅} and P(M₁)=3,     -   and

NBP₂ =P(M ₂)−P(M′ ₂)=4−1=3

with M₂={L₁₂, L₂₃, L₂₄, L₄₅} and P(M₂)=4

-   -   M′₂={L₄₅} and P(M₂)=1,     -   and

NBP₃ =P(M ₃)−P(M′ ₃)=4−3=1

with M₃={L₁₂, L₂₃, L,₂₄, L₄₅} and P(M₃)=4

-   -   M′₃={L₁₂, L,₂₄, L₄₅} and P(M₃)=3,     -   and

NBP₄ =P(M ₄)−P(M′ ₄)=4−2=2

with M₄={L₁₂, L₂₃, L,₂₄, L₄₅} and P(M₄)=4

-   -   M′₄={L₁₂, L,₂₃} and P(M₄)=2,     -   and

NBP₅ =P(M ₅)−P(M′ ₅)=4−3=1

with M₅={L₁₂, L₂₃, L,₂₄, L₄₅} and P(M₅)=4

-   -   M′₅={L₁₂, L,₂₃, L₂₄} and P(M₅)=3.

According to the calculated results, node N₂ has the highest node bridging potential NBP_(i). A higher node bridging potential NBP_(i) relates to a higher impact of the particular node N_(i) on the network. Such higher impact implicates that the particular node N_(i) is directly and indirectly linked to a higher number of nodes N_(i) and/or that the links that the node N_(i) is directly or indirectly connected with comprise higher linking values v_(ij).

Therefore, a higher node bridging potential NBP_(i) correlates to a higher integration of the particular node N_(i) in the set of network nodes N_(i), N_(j). The node bridging potential NBP_(i) preferably affects the node quality score Q_(Ni) of the particular node N_(i), i.e. the node quality score of a particular node is a advantageously a function of its node bridging potential.

To assess the impact of a subset S_(k) of nodes N_(i) on the subset quality score Q_(Sk), the quality evaluation unit 3 is structured to calculate a source bridging potential SBP_(k) of each set S_(k) which is given by the formula (V)

$\begin{matrix} {{SBP}_{k} = {\sum\limits_{{k^{\prime} = 1},\ldots,{s\mspace{14mu} {without}\mspace{14mu} k}}\left( {{P\left( T_{k} \right)} - {P\left( T_{k^{\prime}}^{\prime} \right)}} \right)}} & (V) \end{matrix}$

In Eq. (V), T_(k) is a set containing all links L_(ij) of the set of links L_(ij) that connect nodes N_(j) of the set of nodes N_(i), N_(j) directly or indirectly to at least one node N_(i) in said subset S_(k). T′_(k′) is a set containing the links of T_(k) without the links L_(ij) directly connected to at least one node N_(i) in the subset S_(k) and wherein the links L_(ij) of T′_(k′) connect directly or indirectly to nodes N_(i) of the subset S_(k′). P is a function of the set X of links L_(ij) given by the formula (IV)

$\begin{matrix} {{P(X)} = {\sum\limits_{{links}\mspace{14mu} L_{ij}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {set}\mspace{14mu} X}{v_{ij}.}}} & ({IV}) \end{matrix}$

For the present example, with linking values v_(ij) [v₁₂, v₂₃, v₂₄, v₄₅]=[1, 1, 1, 1], the source bridging potential SBP_(k) for each of the sources S₁, S₂, and S₃ is calculated as following:

For subset S₁, wherein subset S₁={N₁, N₃}:

$\begin{matrix} {{SBP}_{1} = {{\sum\limits_{{k^{\prime} = 2},3}{P\left( T_{1} \right)}} - {P\left( T_{k^{\prime}}^{\prime} \right)}}} \\ {= {\left( {{P\left( T_{1} \right)} - {P\left( T_{2}^{\prime} \right)}} \right) + \left( {{P\left( T_{1} \right)} - {P\left( T_{3}^{\prime} \right)}} \right)}} \\ {= {\left( {4 - 2} \right) + \left( {4 - 2} \right)}} \\ {= 4} \end{matrix}$

because

T ₁ ={L ₁₂ ,L ₂₃ ,L, ₂₄ ,L ₄₅} and P(T ₁)=4

and, for k′=2:

T′ ₂ =L, ₂₄ ,L ₄₅) and P(T′ ₂)=2

and, for k′=3:

T′ ₃ =L, ₂₄ ,L ₄₅) and P(T′ ₃)=2.

For subset S₂, wherein subset S₂={N₂}:

$\begin{matrix} {{SBP}_{2} = {{\sum\limits_{{k^{\prime} = 1},3}{P\left( T_{2} \right)}} - {P\left( T_{k^{\prime}}^{\prime} \right)}}} \\ {= {\left( {{P\left( T_{2} \right)} - {P\left( T_{1}^{\prime} \right)}} \right) + \left( {{P\left( T_{2} \right)} - {P\left( T_{3}^{\prime} \right)}} \right)}} \\ {= {\left( {4 - 0} \right) + \left( {4 - 1} \right)}} \\ {= 7} \end{matrix}$

because

T ₂=(L ₁₂ ,L ₂₃ ,L, ₂₄ ,L ₄₅) and P(T ₂)=4

and, for k′=1:

T′ ₁={ } and P(T′ ₁)=0

and for k′=3:

T′ ₃ ={L ₄₅} and P(T′ ₃)=1.

For subset S₃, wherein subset S₃={N₄, N₅}:

$\begin{matrix} {{SBP}_{3} = {{\sum\limits_{{k^{\prime} = 1},2}{P\left( T_{3} \right)}} - {P\left( T_{k^{\prime}}^{\prime} \right)}}} \\ {= {\left( {{P\left( T_{3} \right)} - {P\left( T_{1}^{\prime} \right)}} \right) + \left( {{P\left( T_{3} \right)} - {P\left( T_{2}^{\prime} \right)}} \right)}} \\ {= {\left( {4 - 2} \right) + \left( {4 - 2} \right)}} \\ {= 4} \end{matrix}$

because

T ₃ ={L ₁₂ ,L ₂₃ ,L, ₂₄ ,L ₄₅} and P(T ₃)=4

and, for k′=1:

T′ ₁ ={L ₁₂ ,L ₂₃} and P(T′ _(i))=2

and, for k′=2:

T′ ₃ ={L ₁₂ ,L ₂₃} and P(T′ ₃)=2.

In summary, the source bridging potential SBP₂ for subset S₂ results in the highest value for the source bridging potential SBP_(i). A high source bridging potential refers to a subset S_(k) which is linking a high number of branches of the network and/or to a subset whose direct or indirect links comprise high linking values v_(ij). The calculation of the source bridging potential SBP_(k) considers not only the linking values v_(ij) of the direct links L_(ij) of the nodes N_(i), N_(j) of the subset S_(k), but also the linking values v_(ij) of the indirect links L_(ij). Therefore, if the nodes of a subset S_(k) link branches which comprise links with higher linking values, the subset S_(k) can achieve a higher source bridging potential SBP value than if the branches themselves are smaller or comprise fewer links with lower linking values.

The source bridging potential SBP of a subset S_(k) is preferably impacting the quality score of the subs set S_(k), i.e. the quality score is advantageously a function of the source bridging potential SBP of a subset S_(k).

The node quality score Q_(Ni) can be calculated for the node N_(i) using the node seeding potential NSP_(i) or the node bridging potential NBP_(i), or a combination thereof.

For the subset S_(k), the subset quality score Q_(Sk) can be calculated from the source seeding potential SSP_(k) or the source bridging potential SBP_(k), or a combination thereof.

The following calculation gives an example of a calculation for the node quality score Q_(Ni) of the node N_(i) as a sum of its node bridging potential and its node seeding potential:

Q _(N1)=NSP₁+NBP₁=4+1=5

Q _(N2)=NSP₂+NBP₂=3+3=6

Q _(N3)=NSP₃+NBP₃=0+1=1

Q _(N4)=NSP₄+NBP₄=1+2=3

Q _(N5)=NSP₅+NBP₅=0+1=1

and for the subset quality score Q_(Sk) of the subset S_(k) we similarly obtain:

Q _(S1)=SSP₁+SBP₁=4+4=8

Q _(S2)=SSP₂+SBP₂=3+7=10

Q _(S3)=SSP₃+SBP₃=1+4=5.

The highest quality score Q_(Ni,max) of the quality scores Q_(Ni) of the nodes N_(i) is therefore the quality score Q_(N2) for the node N₂. The highest quality score Q_(Sk,max) of the quality scores Q_(Sk) for the subsets S_(k) is therefore the quality score Q_(S2) for the subset S₂.

Since a node with a high quality score Q_(Ni,max) might be particularly interesting for the users of the device, a preferred embodiment of the resource allocation unit 14 allocates more computing units 13, 31 and/or more n bandwidth resources and/or storage resources 11, 32 to node N₂ because it has the highest quality score from all nodes, e.g. by providing more hardware infrastructure for node N₂ in order to enable the users e.g. to receive data from the node N₂ more quickly.

A preferred embodiment of the resource allocation unit 14 would furthermore allocate such more computing units 13, 31 and/or more bandwidth resources and/or storage resources 11, 32 also to the subset S₂ with the highest quality score Q_(S2,max) of all subsets, since such subset S₂ might be particular interesting for the users.

FIG. 2c shows the relation of the subsets to each other. In the present example, all subsets S_(k) are linked with each other, either directly or indirectly. A correspondence of the subsets S_(k) can therefore be evaluated, showing a direct or indirect relationship between subset S₁, S₂, and S₃ form the semantic illustration in the figure.

However, it must be noted that, typically, not all subsets in network 10 will connected (directly and indirectly) to each other.

An example of the application of the device 1 would be the implementation of a scientific database. Scientific measurement results, or other types of observations, e.g. from the field of neuroscience, psychological, pharmaceutical or medical sciences, could be evaluated by the device 1. In particular the device is preferably applicable in scientific fields, where experiments are conducted that collect large amounts of data and where it is difficult to analyze the data for its consistency. The invention therefore provides a device that can collect such data and evaluate it.

The nodes N_(i), N_(j) e.g. contain the experimental data collected in measurements and/or the methods applied in the measurements.

The links between the nodes L_(ij) can e.g. be automatically derived from the content of data of the nodes, e.g. the device could be sensitive to keywords in the data that link the node N_(i) to a node N_(j) since both nodes use similar or the same keywords. However, the links can also, at least in part, be entered by the users or by reviewers.

The linking values v_(ij), which are indicative of a correspondence between the nodes N_(i), N_(j), can e.g. be automatically derived from numerical correlation of the measurement results, from the applied methods for executing the measurement, from an overlap in the references given in the measurement results, or from a similarity of conclusions drawn from the measurement data.

A quality score Q can be calculated for evaluating the measurement results. A measurement result is not only given a quality score according to its own content, but also according to its impact in the whole network.

In addition, the measurement data is also evaluated in respect of its agreement or disagreement with the other measurement data in the network, which agreement or disagreement is expressed in the linking values of the links of the particular node comprising the measurement data.

The linking values can also be dependent on a number of downloads of a node, e.g. by attributing a higher linking value v_(ij) for nodes being downloaded more often.

In addition, a further embodiment could also allow the sources or users to control or at least modify the linking values v_(ij), e.g. in a community-based scoring system.

Preferably, the measurement data in the nodes and/or the links between the nodes and/or the linking values thereof and/or the quality score Q of each node and/or each source are published in the public network, such that the users can access this information. For this purpose, the device may comprise a display driver for generating a human-readable representation of this information.

In a further embodiment of the invention, the device can further comprise a data pre-selection unit, which selects measurement data received from the sources according to criteria that could relate to keywords, numbers or methods comprised in the measurement data, and only attributes a node N_(i) of the set of nodes to the measurement data if this data is in accordance with these criteria. In other words, the entry of new nodes can be reviewed automatically. However, a manual review can be used as well.

In one particular embodiment, the pre-selection unit could e.g. calculate the node quality score Q_(Ni) of a potential new node N_(i) and use this node quality score as a criterion for entering the new node into the network. I.e., the node is only inserted e.g. if its node quality score Q_(Ni) exceeds a certain threshold.

While there are shown and described presently preferred embodiments of the invention, it is to be distinctly understood that the invention is not limited thereto but may be otherwise variously embodied and practiced within the scope of the following claims. The embodiments presented in the figures are considered to be illustrative and not restrictive. 

1. A device for evaluating a quality of measurements, the device comprising a data store storing a set of n network nodes N_(i), wherein said nodes store measurement data; a set of l network links L_(ij), wherein each of the network links L_(ij) connects one network node N_(i) with at least one network node N_(j); and a quality evaluation unit for calculating said quality, which quality evaluation unit is adapted and structured to calculate a quality score Q as a function of said links L_(ij).
 2. A device for storing and retrieving measurements comprising a data store storing a set of n network nodes N_(i), wherein said nodes store measurement data; a set of l network links L_(ij), wherein each of the network links L_(ij) connects one network node N_(i) with one network node N_(j); and a quality evaluation unit for calculating a quality score Q as a function of said links L_(ij), a resource allocation unit for allocating computing units, bandwidth and/or storage resources for processing the nodes as a function of said quality score Q.
 3. Device according to claim 2 connectable to a plurality of computing units, wherein said resource allocation unit is adapted and structured to allocate a number z of said computing units for processing a given node, wherein said number z depends on the quality score Q.
 4. Device according to any claim 2, wherein the resource allocation unit is adapted and structured to allocate bandwidth resources for processing a given node, wherein said allocated bandwidth resources depend on the quality score Q.
 5. Device according to claim 2, wherein said data store comprises a plurality of storage resources, in particular storage resources with different performance, wherein said resource allocation unit is adapted and structured to select the storage resources for storing each node of the set as a function of said quality score Q.
 6. The device according to claim 2, wherein said data store comprises, for each of said nodes, information indicative of at least one subset S_(k) of nodes that a node belongs to, and wherein said quality evaluation unit is adapted and structured for calculating a subset quality score Q_(Sk) for at least said subset S_(k) as a function of said links L_(ij).
 7. The device according to claim 1, wherein said quality evaluation unit is adapted and structured for calculating a node quality score Q_(Ni) for at least one node N_(i) of said set of nodes as a function of said links L_(ij).
 8. The device according to claim 2, wherein each link L_(ij) comprises a linking value v_(ij) indicative of a correspondence between said nodes N_(i) and N_(j), and in particular wherein the linking values v_(ij) are scalar values.
 9. The device according to claim 2, wherein the linking value v_(ij) is larger for a higher correspondence between said nodes N_(i) and N_(j) as compared to the linking value v_(ij) for a lower correspondence between said nodes N_(i) and N_(j), wherein, if at least one of said nodes N_(i) and N_(j) is in said subset S_(k), said subset quality score Q_(Sk) increases with an increasing value of said linking value v_(ij), wherein said resource allocation unit is adapted and structured to allocate an amount of the computing units, the bandwidth or the storage resources for processing said subset S_(k), which amount is monotonically increasing with said quality score Q_(Sk).
 10. Device according to claim 2, wherein said network links L_(ij) comprise directional links indicating that a node N_(j) depends on a node N_(i) wherein the quality evaluation unit is structured to calculate said quality score Q as a function of a node seeding potential NSP_(i) of at least one of said nodes N_(i), wherein said node seeding potential NSP_(i) of said first node N_(i) is given by NSP_(i) =|C _(i)| with C_(i) being a set of nodes containing all nodes N_(j) depending directly or indirectly on a node N_(i) and | . . . | being the cardinality of C_(i).
 11. Device of claim 6, wherein the quality evaluation unit is structured to calculate said quality score Q as a function of a source seeding potential SSP_(k) of said subset S_(k) as a function of ${SSP}_{k} = {\sum\limits_{N_{i}\mspace{14mu} i\; n\mspace{14mu} S_{k}}{{NSP}_{i}.}}$
 12. Device according to claim 1, wherein the quality evaluation unit is structured to calculate said quality score Q as a function of a node bridging potential NBP_(i) of at least one node N_(i) of said nodes, wherein said node bridging potential NBP_(i) is given by NBP_(i) =P(M _(i))−P(M′ _(i)) with M_(i) being a set of links L_(m) containing all links L_(ij) that connect the nodes N_(j) of the set of nodes directly or indirectly to said node N_(ij), M′_(i) being said set M_(i) of links L_(m) without the links L_(ij) to said node N_(i), and P being a function of a set X of links given by ${P(X)} = {\sum\limits_{{links}\mspace{14mu} L_{ij}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {set}\mspace{14mu} X}{v_{ij}.}}$
 13. Device according to claim 6, wherein the quality evaluation unit is structured to calculate said quality score Q as a function of a source bridging potential SBP_(k) of said subset S_(k) of the s subsets S_(k) wherein said source bridging potential SBP_(k) is given by ${SBP}_{k} = {\sum\limits_{{k^{\prime} = 1},\ldots,{s\mspace{14mu} {without}\mspace{14mu} k}}\left( {{P\left( T_{k} \right)} - {P\left( T_{k^{\prime}}^{\prime} \right)}} \right)}$ with T_(k) being a set containing all links L_(ij) of the set of links L_(ij) that connect nodes N_(i) of the set of nodes N_(i), N_(j) directly or indirectly to at least one node N_(i) in said subset S_(k); and T′_(k′) contains the links of said set T_(k) without the links L_(ij) directly connected to at least one node N_(i) in said subset S_(k), and wherein the links L_(ij) of T′_(k′) connect directly or indirectly to nodes N_(i) of the subset S_(k)′; and P being a function of a set X of links given by ${P(X)} = {\sum\limits_{{links}\mspace{14mu} L_{ij}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {set}\mspace{14mu} X}{v_{ij}.}}$
 14. Device according to claim 10, wherein the quality evaluation unit is structured to calculate the quality score Q as a combination of one or more of the node seeding potential NSP, the source seeding potential SSP, the node bridging potential NBP, and/or the source bridging potential SBP.
 15. Device according to claim 1, wherein the quality evaluation unit is further structured to calculate at least a first quality score Q of a first node or subset and a second quality score Q of a second node or subset, and to evaluate a node or subset S_(max) with a highest quality score Q_(max).
 16. Device according to claim 6, wherein each one of said subsets S_(k) contains a single node N_(i).
 17. Device according to claim 6 wherein at least one of said subsets S_(k) contains more than one node N_(i).
 18. A method for evaluating measurements using the device of claim 1, the method comprising: storing said measurement data in said nodes N_(i), N_(j), storing said links L_(ij), calculating said quality score Q for evaluating the quality of said measurement data.
 19. Method according to claim 18 further comprising: executing, by means of several sources, a number of measurements, storing measurement data from said sources in said nodes N_(i), N_(j), storing, for each of said nodes, information indicative of at least one subset S_(k) of nodes that a node belongs to, with said subsets S_(k) corresponding to said sources U_(k), linking said nodes N_(i), N_(j) by said network links L_(ij). 